Current Advances in LLM Reasoning

A unified, hands-on tour of how well LLMs reason, how to make them reason better, and where the field is heading next. Presented as a half-day tutorial at three venues in 2026.

Overview

As Large Language Models (LLMs) increasingly tackle reasoning-heavy tasks, from mathematics to commonsense to multilingual understanding, researchers face three pressing questions: How well do models reason? How can we make them reason better? And what are the next frontiers in LLM reasoning?

This tutorial answers these questions through a unified view of LLM reasoning. We explore comprehensive evaluation strategies to assess the reasoning abilities of models and discuss two families of methods that improve reasoning: advanced inference-time methods and post-training methods.

The tutorial is designed for both researchers and practitioners seeking actionable insight into LLM reasoning.

Venues

The tutorial runs at three venues in 2026.

Q&A & Discussion

Have a question about the tutorial, the slides, or the hands-on materials? Want to share ideas or connect with others working on LLM reasoning? Join the conversation on GitHub Discussions.